About ‘Engineering Serendipity’ for April 2021

Engineering serendipity: when does knowledge sharing lead to knowledge production?


Research Summary

We investigate how knowledge similarity between two individuals is systematically related to the likelihood that a serendipitous encounter results in knowledge production. We conduct a field experiment at a medical research symposium, where we exogenously varied opportunities for face‐to‐face encounters among 15,817 scientist‐pairs. Our data include direct observations of interaction patterns collected using sociometric badges, and detailed, longitudinal data of the scientists’ postsymposium publication records over 6 years. We find that interacting scientists acquire more knowledge and coauthor 1.2 more papers when they share some overlapping interests, but cite each other’s work between three and seven times less when they are from the same field. Our findings reveal both collaborative and competitive effects of knowledge similarity on knowledge production outcomes.

Managerial Summary

Managers often try to stimulate innovation by encouraging serendipitous interactions between employees, for example by using office space redesigns, conferences and similar events. Are such interventions effective? This article proposes that an effective encounter depends on the degree of common knowledge shared by the individuals. We find that scientists who attend the same conference are more likely to learn from each other and collaborate effectively when they have some common interests, but may view each other competitively when they work in the same field. Hence, when designing opportunities for face‐to‐face interactions, managers should consider knowledge similarity as a criteria for fostering more productive exchanges.

Questions for Twitter

  • You introduce an engineered serendipity as a way of avoiding costs of collaborations. Do you think regular serendipity has a cost?
  • You emphasise the inverted U nature of the results in your paper where too much distance between pairs and too much similarity are both detrimental. Do you think this might also translate to skill levels in individual serendipity?
  • You use a prospective research design. What do you see as the benefits of such a design? Is it a big risk?
  • Finally, can you engineer serendipity?

Citation & Links:

Lane, J., Ganguli, I., Gaule, P., Guinan, E., & Lakhani, K.R. Engineering serendipity: When does knowledge sharing lead to knowledge production? Strategic Management Journal, Early View online: https://doi.org/10.1002/smj.3256

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